Cross-Compiling the Dependencies Pieter P

Total Page:16

File Type:pdf, Size:1020Kb

Cross-Compiling the Dependencies Pieter P Cross-Compiling the Dependencies Pieter P Sysroot and Staging Area Because we don't have access to the actual root directory of the Raspberry Pi, with all of its system les and libraries, the toolchain uses a so-called sysroot folder. It contains the necessary system libraries, such as glibc and the C++ standard library. It's also the folder where the congure scripts of other libraries will look for the necessary libraries and headers. The sysroot is generated by Crosstool-NG, and it is part of the cross-compilation toolchain. It is not used for deploying your software to the Pi. The sysroot of the toolchain is read-only, to keep it clean for future projects, so we'll make a copy of the sysroot for this build, and make it writable. This copy will be used as the sysroot for all compilations, and all cross-compiled libraries will be installed to this folder. We have to do this, because the congure scripts (Autoconf's congure script, CMake, etc.) of other libraries or programs will search for their dependencies in the sysroot. If these dependencies are not found, conguration or compilation will fail, or parts of the program/library may be implicitly disabled. Apart from the sysroot, we also need a folder containing the les we want to install to the Pi. It should contain the binaries we cross- compiled (such as /usr/local/bin/python3.8) and the necessary libraries (such as /usr/local/lib/libpython3.8.so). It doesn't contain the system libraries, because the Pi already has these installed. This folder is called the staging area. Having both a sysroot and a staging area means we have to install every library twice, once in each of the two folders. The sysroot is later used when compiling our program, the staging area will be copied to the Pi for running our program. Cross-Compiling the dependencies using the provided shell scripts To build all dependencies, you can use the shell script provided in the toolchain folder: $ ./toolchain/toolchain.sh <board> --export Where <board> is one of the following: rpi: Raspberry Pi 1 or Zero, dependencies only rpi-dev: Raspberry Pi 1 or Zero, dependencies and development tools rpi3-armv8: Raspberry Pi 3, 32-bit, dependencies only rpi3-armv8-dev: Raspberry Pi 3, 32-bit, dependencies and development tools rpi3-aarch64: Raspberry Pi 3, 64-bit, dependencies only rpi3-aarch64-dev: Raspberry Pi 3, 64-bit, dependencies and development tools The dependencies that are cross-compiled are: Zlib: compression library (OpenSSL and Python dependency) OpenSSL: cryptography library (Python dependency) FFI: foreign function interface (Python dependency, used to call C functions using ctypes) Bzip2: compression library (Python dependency) GNU ncurses: library for text-based user interfaces (Python dependency, used for the console) GNU readline: library for line-editing and history (Python dependency, used for the console) GNU dbm: library for key-value data (Python dependency) SQLite: library for embedded databases (Python dependency) UUID: library for unique identiers (Python dependency) libX11: X11 protocol client library (Tk dependency) Tcl/Tk: graphical user interface toolkit (Python/Tkinter dependency) Python 3.8.3: Python interpreter and libraries ZBar: Bar and QR code decoding library Raspberry Pi Userland: VideoCore GPU drivers VPX: VP8/VP9 codec SDK x264: H.264/MPEG-4 AVC encoder Xvid: MPEG-4 video codec FFmpeg: library to record, convert and stream audio and video OpenBLAS: linear algebra library (NumPy dependency) NumPy: multi-dimensional array container for Python (OpenCV dependency) SciPy: Python module for mathematics, science, and engineering OpenCV 4.3.0: computer vision library and Python module The development tools that are cross-compiled are: GCC 9.2.0: C, C++ and Fortran compilers (see native toolchain on the previous page) GNU Make: build automation tool Ninja: faster, more light-weight build tool CMake: build system 1 Distcc: distributed compiler wrapper (uses your computer to speed up compilation on the RPi) CCache: compiler cache cURL: tool and library for transferring data over the network (Git dependency) Git: version control system Pulling the cross-compiled dependencies from Docker Hub If you don't want to change anything to the build process, or if you have a slow computer, you can just pull the Docker images that I compiled from Docker Hub: $ ./toolchain/toolchain.sh <board> --pull --export Detailed information about cross-compilation of libraries You don't need to read or understand the sections below if you just want to use the provided libraries. On the other hand, if you're interested in how cross-compilation works, or if you want to cross-compile additional libraries, you'll nd the necessary details below. Base Image Before building the dependencies, I created a base image with Ubuntu and the necessary tools installed. I also created a non-root user. Dockerfile 1 FROM ubuntu:latest as rpi-cpp-toolchain-base-ubuntu 2 3 # Install some tools and compilers + clean up 4 RUN apt-get update && \ 5 apt-get install -y sudo git wget \ 6 gcc g++ cmake make autoconf automake \ 7 gperf diffutils bzip2 libbz2-dev xz-utils \ 8 flex gawk help2man libncurses-dev patch bison \ 9 python-dev gnupg2 texinfo unzip libtool-bin \ 10 autogen libtool m4 gettext pkg-config && \ 11 apt-get clean autoclean && \ 12 apt-get autoremove -y && \ 13 rm -rf /var/lib/apt/lists/* 14 15 # Add a user called `develop`, and add him to the sudo group 16 RUN useradd -m develop && \ 17 echo "develop:develop" | chpasswd && \ 18 adduser develop sudo 19 20 USER develop 21 WORKDIR /home/develop Cross-Compiling the dependencies If you want to write a program that uses the OpenCV library, you have to cross-compile OpenCV and install it to the Raspberry Pi. But in order to cross-compile OpenCV itself, you need to cross-compile all of its dependencies as well. This can keep going for a while, and you may end up with a pretty large hierarchy of dependencies. The main Dockerle discussed below sets up the build environment, and then runs the installation scripts in the toolchain/docker/merged/cross-build/install-scripts folder. If you want to omit some of the libraries, you can comment them out in the Dockerle, but keep in mind that it might break other libraries that depend on it. Some libraries such as SciPy need to be patched to get them to cross-compile correctly. These patches can be found in the patches folder. For most packages, the build procedure is very simple: 1. Download 2. Extract 3. Run the configure script with the right options 4. make 5. make install An example is given below. Board-specic conguration The conguration for the different Raspberry Pi models is passed to the build scripts using environment variables. For example: aarch64-rpi3-linux-gnu.env 2 1 export CROSS_TOOLCHAIN_IMAGE="aarch64-rpi3-linux-gnu" 2 export HOST_ARCH="aarch64" 3 export HOST_TRIPLE="aarch64-rpi3-linux-gnu" 4 export HOST_TRIPLE_NO_VENDOR="aarch64-linux-gnu" 5 export HOST_TRIPLE_LIB_DIR="aarch64-linux-gnu" 6 export HOST_BITNESS=64 Compiling a library for the Docker container In the rst section of the main Dockerle, some packages are built for both the build machine (the Docker container) and for the host machine (the Raspberry Pi), because we need to build Python for both machines in order to cross-compile the OpenCV and NumPy modules later on. Since these are just basic native installations, you can simply follow the documentation for the library in question. All of the necessary tools should be installed in the base image. Cross-Compiling a library for the Raspberry Pi Cross-compiling is a bit harder, because most libraries don't provide good documentation about the cross-compilation process. However, once you understand the concepts, you'll be able to apply them to almost any library, and with the necessary tweaks, you should be able to get it working. We'll have a look at a typical example, compiling libffi, a foreign function interface library that is used by Python's ctypes module. libffi.sh 1 #!/usr/bin/env bash 2 3 set -ex 4 5 # Download 6 version=3.4.2 7 URL="https://codeload.github.com/libffi/libffi/tar.gz/v$version" 8 pushd "${DOWNLOADS}" 9 wget -N "$URL" -O libffi-$version.tar.gz 10 popd 11 12 # Extract 13 tar xzf "${DOWNLOADS}/libffi-$version.tar.gz" 14 pushd libffi-$version 15 16 # Configure 17 . cross-pkg-config 18 ./autogen.sh 19 ./configure \ 20 --host="${HOST_TRIPLE}" \ 21 --prefix="/usr/local" \ 22 CFLAGS="-O3" \ 23 CXXFLAGS="-O3" \ 24 --with-sysroot="${RPI_SYSROOT}" 25 26 # Build 27 make -j$(($(nproc) * 2)) 28 29 # Install 30 make install DESTDIR="${RPI_SYSROOT}" 31 make install DESTDIR="${RPI_STAGING}" 32 33 # Cleanup 34 popd 35 rm -rf libffi-$version Downloading and extracting The rst couple of lines are really straightforward, they simply download the library from GitHub, extract it, and enter the library's directory. pkg-cong Most congure scripts use pkg-config, a simple tool to nd libraries that are installed on the system, and to determine what ags are necessary to use them. We want pkg-config to nd the libraries in the Raspberry Pi sysroot, not in the root folder of the Docker container, because these libraries are for the wrong architecture. The cross-pkg-config script that is sourced on line 16 sets some environment variables in order to tell pkg-config to only search for libraries in the Raspberry Pi sysroot. Autoconf Many older libraries use GNU Autoconf to congure the project before building. Autoconf generates the configure script, and then the configure script generates the makeles that are used to actually compile and install the software.
Recommended publications
  • Libffi This Manual Is for Libffi, a Portable Foreign-Function Interface Library
    Libffi This manual is for Libffi, a portable foreign-function interface library. Copyright c 2008, 2010, 2011 Red Hat, Inc. Permission is granted to copy, distribute and/or modify this document under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2, or (at your option) any later version. A copy of the license is included in the section entitled \GNU General Public License". Chapter 2: Using libffi 1 1 What is libffi? Compilers for high level languages generate code that follow certain conventions. These conventions are necessary, in part, for separate compilation to work. One such convention is the calling convention. The calling convention is a set of assumptions made by the compiler about where function arguments will be found on entry to a function. A calling convention also specifies where the return value for a function is found. The calling convention isalso sometimes called the ABI or Application Binary Interface. Some programs may not know at the time of compilation what arguments are to be passed to a function. For instance, an interpreter may be told at run-time about the number and types of arguments used to call a given function. `Libffi' can be used in such programs to provide a bridge from the interpreter program to compiled code. The `libffi' library provides a portable, high level programming interface to various calling conventions. This allows a programmer to call any function specified by a call interface description at run time. FFI stands for Foreign Function Interface. A foreign function interface is the popular name for the interface that allows code written in one language to call code written in another language.
    [Show full text]
  • Dell Wyse Management Suite Version 2.1 Third Party Licenses
    Dell Wyse Management Suite Version 2.1 Third Party Licenses October 2020 Rev. A01 Notes, cautions, and warnings NOTE: A NOTE indicates important information that helps you make better use of your product. CAUTION: A CAUTION indicates either potential damage to hardware or loss of data and tells you how to avoid the problem. WARNING: A WARNING indicates a potential for property damage, personal injury, or death. © 2020 Dell Inc. or its subsidiaries. All rights reserved. Dell, EMC, and other trademarks are trademarks of Dell Inc. or its subsidiaries. Other trademarks may be trademarks of their respective owners. Contents Chapter 1: Third party licenses...................................................................................................... 4 Contents 3 1 Third party licenses The table provides the details about third party licenses for Wyse Management Suite 2.1. Table 1. Third party licenses Component name License type jdk1.8.0_112 Oracle Binary Code License jre11.0.5 Oracle Binary Code License bootstrap-2.3.2 Apache License, Version 2.0 backbone-1.3.3 MIT MIT aopalliance-1.0.jar Public Domain aspectjweaver-1.7.2.jar Eclipse Public licenses- v 1.0 bcprov-jdk16-1.46.jar MIT commons-codec-1.9.jar Apache License, Version 2.0 commons-logging-1.1.1.jar Apache License, Version 2.0 hamcrest-core-1.3.jar BSD-3 Clause jackson-annotations.2.10.2.jar Apache License, Version 2.0 The Apache Software License, Version 2.0 jackson-core.2.10.2.jar Apache License, Version 2.0 The Apache Software License, Version 2.0 jackson-databind.2.10.2.jar Apache License, Version 2.0 The Apache Software License, Version 2.0 log4j-1.2.17.jar Apache License, Version 2.0 mosquitto-3.1 Eclipse Public licenses- v 1.0 Gradle Wrapper 2.14 Apache 2.0 License Gradle Wrapper 3.3 Apache 2.0 License HockeySDK-Ios3.7.0 MIT Relayrides / pushy - v0.9.3 MIT zlib-1.2.8 zlib license yaml-cpp-0.5.1 MIT libssl.dll (1.1.1c) Open SSL License 4 Third party licenses Table 1.
    [Show full text]
  • CFFI Documentation Release 0.8.6
    CFFI Documentation Release 0.8.6 Armin Rigo, Maciej Fijalkowski May 19, 2015 Contents 1 Installation and Status 3 1.1 Platform-specific instructions......................................4 2 Examples 5 2.1 Simple example (ABI level).......................................5 2.2 Real example (API level).........................................5 2.3 Struct/Array Example..........................................6 2.4 What actually happened?.........................................6 3 Distributing modules using CFFI7 3.1 Cleaning up the __pycache__ directory.................................7 4 Reference 9 4.1 Declaring types and functions......................................9 4.2 Loading libraries............................................. 10 4.3 The verification step........................................... 10 4.4 Working with pointers, structures and arrays.............................. 12 4.5 Python 3 support............................................. 14 4.6 An example of calling a main-like thing................................. 15 4.7 Function calls............................................... 15 4.8 Variadic function calls.......................................... 16 4.9 Callbacks................................................. 17 4.10 Misc methods on ffi........................................... 18 4.11 Unimplemented features......................................... 20 4.12 Debugging dlopen’ed C libraries..................................... 20 4.13 Reference: conversions.......................................... 21 4.14 Reference:
    [Show full text]
  • Compiler Fuzzing: How Much Does It Matter?
    Compiler Fuzzing: How Much Does It Matter? MICHAËL MARCOZZI∗, QIYI TANG∗, ALASTAIR F. DONALDSON, and CRISTIAN CADAR, Imperial College London, United Kingdom Despite much recent interest in randomised testing (fuzzing) of compilers, the practical impact of fuzzer-found compiler bugs on real-world applications has barely been assessed. We present the first quantitative and qualitative study of the tangible impact of miscompilation bugs in a mature compiler. We follow a rigorous methodology where the bug impact over the compiled application is evaluated based on (1) whether the bug appears to trigger during compilation; (2) the extent to which generated assembly code changes syntactically due to triggering of the bug; and (3) whether such changes cause regression test suite failures, or whether we can manually find application inputs that trigger execution divergence due to such changes. Thestudy is conducted with respect to the compilation of more than 10 million lines of C/C++ code from 309 Debian 155 packages, using 12% of the historical and now fixed miscompilation bugs found by four state-of-the-art fuzzers in the Clang/LLVM compiler, as well as 18 bugs found by human users compiling real code or as a by-product of formal verification efforts. The results show that almost half of the fuzzer-found bugs propagate tothe generated binaries for at least one package, in which case only a very small part of the binary is typically affected, yet causing two failures when running the test suites of all the impacted packages. User-reported and formal verification bugs do not exhibit a higher impact, with a lower rate of triggered bugs andonetest failure.
    [Show full text]
  • VSI's Open Source Strategy
    VSI's Open Source Strategy Plans and schemes for Open Source so9ware on OpenVMS Bre% Cameron / Camiel Vanderhoeven April 2016 AGENDA • Programming languages • Cloud • Integraon technologies • UNIX compability • Databases • Analy;cs • Web • Add-ons • Libraries/u;li;es • Other consideraons • SoDware development • Summary/conclusions tools • Quesons Programming languages • Scrip;ng languages – Lua – Perl (probably in reasonable shape) – Tcl – Python – Ruby – PHP – JavaScript (Node.js and friends) – Also need to consider tools and packages commonly used with these languages • Interpreted languages – Scala (JVM) – Clojure (JVM) – Erlang (poten;ally a good fit with OpenVMS; can get good support from ESL) – All the above are seeing increased adop;on 3 Programming languages • Compiled languages – Go (seeing rapid adop;on) – Rust (relavely new) – Apple Swi • Prerequisites (not all are required in all cases) – LLVM backend – Tweaks to OpenVMS C and C++ compilers – Support for latest language standards (C++) – Support for some GNU C/C++ extensions – Updates to OpenVMS C RTL and threads library 4 Programming languages 1. JavaScript 2. Java 3. PHP 4. Python 5. C# 6. C++ 7. Ruby 8. CSS 9. C 10. Objective-C 11. Perl 12. Shell 13. R 14. Scala 15. Go 16. Haskell 17. Matlab 18. Swift 19. Clojure 20. Groovy 21. Visual Basic 5 See h%p://redmonk.com/sogrady/2015/07/01/language-rankings-6-15/ Programming languages Growing programming languages, June 2015 Steve O’Grady published another edi;on of his great popularity study on programming languages: RedMonk Programming Language Rankings: June 2015. As usual, it is a very valuable piece. There are many take-away from this research.
    [Show full text]
  • Structured Foreign Types
    Ftypes: Structured foreign types Andrew W. Keep R. Kent Dybvig Indiana University fakeep,[email protected] Abstract When accessing scalar elements within an ftype, the value of the High-level programming languages, like Scheme, typically repre- scalar is automatically marshaled into the equivalent Scheme rep- sent data in ways that differ from the host platform to support resentation. When setting scalar elements, the Scheme value is consistent behavior across platforms and automatic storage man- checked to ensure compatibility with the specified foreign type, and agement, i.e., garbage collection. While crucial to the program- then marshaled into the equivalent foreign representation. Ftypes ming model, differences in data representation can complicate in- are well integrated into the system, with compiler support for ef- teraction with foreign programs and libraries that employ machine- ficient access to foreign data and debugger support for convenient dependent data structures that do not readily support garbage col- inspection of foreign data. lection. To bridge this gap, many high-level languages feature for- The ftype syntax is convenient and flexible. While it is similar in eign function interfaces that include some ability to interact with some ways to foreign data declarations in other Scheme implemen- foreign data, though they often do not provide complete control tations, and language design is largely a matter of taste, we believe over the structure of foreign data, are not always fully integrated our syntax is cleaner and more intuitive than most. Our system also into the language and run-time system, and are often not as effi- has a more complete set of features, covering all C data types along cient as possible.
    [Show full text]
  • Overview Guide Release 21B F45060-01
    Oracle Utilities Customer Care and Billing Cloud Service Overview Guide Release 21B F45060-01 August 2021 Oracle Utilities Customer Care and Billing Cloud Service Release 21B Overview Guide Copyright © 2012, 2021 Oracle and/or its affiliates. All rights reserved. This software and related documentation are provided under a license agreement containing restrictions on use and disclosure and are protected by intellectual property laws. Except as expressly permitted in your license agreement or allowed by law, you may not use, copy, reproduce, translate, broadcast, modify, license, transmit, distribute, exhibit, perform, publish, or display any part, in any form, or by any means. Reverse engineering, disassembly, or decompilation of this software, unless required by law for interoperability, is prohibited. The information contained herein is subject to change without notice and is not warranted to be error-free. If you find any errors, please report them to us in writing. If this is software or related documentation that is delivered to the U.S. Government or anyone licensing it on behalf of the U.S. Government, then the following notice is applicable: U.S. GOVERNMENT END USERS: Oracle programs (including any operating system, integrated software, any programs embedded, installed or activated on delivered hardware, and modifications of such programs) and Oracle computer documentation or other Oracle data delivered to or accessed by U.S. Government end users are "commercial computer software" or "commercial computer software documentation"
    [Show full text]
  • Shiny Server Administrator's Guide
    Shiny Server Administrator’s Guide Shiny Server Professional v1.5.5 Copyright © 2017 RStudio, Inc. Contents 1 Getting Started 8 1.1 Introduction.........................................8 1.2 System Requirements...................................9 1.3 Installation.........................................9 1.3.1 Ubuntu (12.04+)..................................9 1.3.2 RedHat/CentOS (5.4+).............................. 10 1.3.3 SUSE Linux Enterprise Server (11+)....................... 11 1.3.4 Install Shiny.................................... 12 1.3.5 R Installation Location.............................. 13 1.4 Stopping and Starting................................... 14 1.4.1 systemd (RedHat 7, Ubuntu 15.04+, SLES 12+)................ 14 1.4.2 Upstart (Ubuntu 12.04 through 14.10, RedHat 6)................ 15 1.4.3 init.d (RedHat 5, SLES 11)............................ 16 2 Server Management 17 2.1 Default Configuration................................... 17 2.2 Server Hierarchy...................................... 18 2.2.1 Server........................................ 18 2.2.2 Location....................................... 19 2.2.3 Application..................................... 20 2.3 run_as ............................................ 20 2.3.1 :HOME_USER: .................................... 21 2.3.2 Running Shiny Server with Root Privileges................... 22 2.3.3 :AUTH_USER: .................................... 23 2.4 PAM Sessions........................................ 25 2.4.1 Session Profile..................................
    [Show full text]
  • An In-Memory Embedding of Cpython for Offensive Use
    An In-memory Embedding of CPython for Offensive Use Ateeq Sharfuddin, Brian Chapman, Chris Balles SCYTHE fateeq, brian, [email protected] Abstract—We offer an embedding of CPython that runs using packagers like Py2Exe [6] or PyInstaller [4]. These entirely in memory without “touching” the disk. This in-memory malware are easy to construct and are executed when a coerced embedding can load Python scripts directly from memory instead end-user double-clicks and runs the packaged executable file these scripts having to be loaded from files on disk. Malware that resides only in memory is harder to detect or mitigate against. from disk. Security products have collected and analyzed We intend for our work to be used by security researchers to numerous sample executable files of this scheme. As a result, rapidly develop and deploy offensive techniques that is difficult security products have produced signatures for this scheme and for security products to analyze given these instructions are are able to successfully block all malware using this scheme. in bytecode and only translated to machine-code by the inter- In this paper, we contribute the first embedding of CPython preter immediately prior to execution. Our work helps security researchers and enterprise Red Teams who play offense. Red interpreter that runs entirely in memory without “touching” Teams want to rapidly prototype malware for their periodic the disk. We also contribute a scheme to support loading campaigns and do not want their malware to be detected by the and running Python modules including native C extensions, Incident Response (IR) teams prior to accomplishing objectives.
    [Show full text]
  • Foreign Library Interface by Daniel Adler Dia Applications That Can Run on a Multitude of Plat- Forms
    30 CONTRIBUTED RESEARCH ARTICLES Foreign Library Interface by Daniel Adler dia applications that can run on a multitude of plat- forms. Abstract We present an improved Foreign Function Interface (FFI) for R to call arbitary na- tive functions without the need for C wrapper Foreign function interfaces code. Further we discuss a dynamic linkage framework for binding standard C libraries to FFIs provide the backbone of a language to inter- R across platforms using a universal type infor- face with foreign code. Depending on the design of mation format. The package rdyncall comprises this service, it can largely unburden developers from the framework and an initial repository of cross- writing additional wrapper code. In this section, we platform bindings for standard libraries such as compare the built-in R FFI with that provided by (legacy and modern) OpenGL, the family of SDL rdyncall. We use a simple example that sketches the libraries and Expat. The package enables system- different work flow paths for making an R binding to level programming using the R language; sam- a function from a foreign C library. ple applications are given in the article. We out- line the underlying automation tool-chain that extracts cross-platform bindings from C headers, FFI of base R making the repository extendable and open for Suppose that we wish to invoke the C function sqrt library developers. of the Standard C Math library. The function is de- clared as follows in C: Introduction double sqrt(double x); We present an improved Foreign Function Interface The .C function from the base R FFI offers a call (FFI) for R that significantly reduces the amount of gate to C code with very strict conversion rules, and C wrapper code needed to interface with C.
    [Show full text]
  • Poliastro: an Astrodynamics Library Written in Python with Fortran Performance
    POLIASTRO: AN ASTRODYNAMICS LIBRARY WRITTEN IN PYTHON WITH FORTRAN PERFORMANCE Juan Luis Cano Rodríguez Helge Eichhorn Frazer McLean Universidad Politécnica de Madrid Technische Universität Darmstadt CS GmbH Madrid, Spain Darmstadt, Germany Darmstadt, Germany ABSTRACT However, the current situation of scientific codes is still delicate: most scientists and engineers that write numerical Python is a fast-growing language both for astronomic appli- software (which often features a strong algorithmic com- cations and for educational purposes, but it is often criticized ponent and has tight performance requirements) usually do for its suboptimal performance and lack of type enforcement. not have any formal training on computer programming, let In this paper we present poliastro, a pure Python library for alone software engineering best practices[3]. In fact, in the Astrodynamics that overcomes these obstacles and serves as Aerospace industry there is a sad track record of software a proof of concept of Python strengths and its suitability to failures[4, 5] that could have been avoided by following model complex systems and implement fast algorithms. better software and systems engineering practices. poliastro features core Astrodynamics algorithms (such as resolution of the Kepler and Lambert problems) written When selecting a certain programming language for a spe- in pure Python and compiled using numba, a modern just- cific problem, we as engineers have the obligation to consider in-time Python-to-LLVM compiler. As a result, preliminary as much information as possible and make an informed de- benchmarks suggest a performance increase close to the ref- cision based on technical grounds. For example, if defect erence Fortran implementation, with negligible impact in the density were to be selected as the single figure to rank the legibility of the Python source.
    [Show full text]
  • Licensing Information User Manual Mysql Enterprise Monitor 3.4
    Licensing Information User Manual MySQL Enterprise Monitor 3.4 Table of Contents Licensing Information .......................................................................................................................... 4 Licenses for Third-Party Components .................................................................................................. 5 Ant-Contrib ............................................................................................................................... 10 ANTLR 2 .................................................................................................................................. 11 ANTLR 3 .................................................................................................................................. 11 Apache Commons BeanUtils v1.6 ............................................................................................. 12 Apache Commons BeanUtils v1.7.0 and Later ........................................................................... 13 Apache Commons Chain .......................................................................................................... 13 Apache Commons Codec ......................................................................................................... 13 Apache Commons Collections .................................................................................................. 14 Apache Commons Daemon ...................................................................................................... 14 Apache
    [Show full text]